ICAR keynote presentations Genomic selection and its ... · 9. Juni 2010 Seite 1 ICAR keynote...

9. Juni 2010 Seite 1

ICAR keynote presentations

Genomic selection and its potential to change cattle breeding

Reinhard Reents

- Chairman of the Steering Committee of Interbull- Secretary of ICAR- General Manager of vit, IT solutions for animal production

N:zws/folien/rreents/genetic_evaluation_riga2010_reents.ppt


Outline

Where we come from

New opportunities due to genomics

Challenges for breeding programs

Outlook


Selection from about 1960 to ~ 2009

Quantitative-genetic concepts(Wright, Lush, Henderson) -> additiv genetic model

Genetic evaluationSeparate phenotypic observations (eg 9850 kg milk) in

• additiv genetic effect estimated breeding value (eg. + 1430 kg M)• Systematic environmental effect• Residual effect

Ranking based on estimated breeding values (EBVs)


A large proportion of genetic gain is achieved by a very high selection intensityon the male side (one bull returning to service out of ~10-15 test bulls

with 60.000 to 100.000 doses of semen per proven bull and year)

Necessary elements for this ‚traditional breeding program‘Phenotypic observations

• Milk yield, somatic cell counts, type traits, etc. Pedigree dataData structure (across herds/environments)

Artificial insemination gives optimal structure to estimate EBVs that rank theanimals best and unbiased in many environments

• Algorithms (Henderson, Schaeffer&Kennedy, Misztal, etc) and computing power

BLUP methodology, which result in highly reliable EBVs (85-99%) for bullswith a progeny test of 100-150 daughtersTransformation of these EBVs since 15 years via Interbull MACE

Bulls that are marketed worldwide

Selection on EBVs in AI breeding programs


1995 Production

1999 Production Type

Workability

2001 Production Type Cellcount

2004 Production Type Cellcount Longevity

2008 Production Type Cellcount Longevity Calving Fertility

2005 Production Type Cellcount Longevity Calving

2007 Production Type Cellcount Longevity Calving Fertility

Portfolio of Interbull evaluations


Genetic trend in Holstein bulls(MACE Evaluation Interbull, Holstein AI bulls (50,000); Mean = 100, SD = 10; Data Sept. 2007, Berglund, 2008)

EBV

PROT=Protein yield Jorjani, 2008

76

82

88

94

100

106

112

1985 1990 1995 2000

PROT

Year of birth of AI bulls


but

Genetic gain / costsHigh generation interval due to progeny test

Expensive, due to keeping of many waiting bulls Genetic gain per year not very high

Reasons for this: Although one can select young bulls for progeny test on very reliable EBVs

• (bull dams ~60% r² and bull sires ~95% r²)reliability of a pedigree index =0,5 EBV sire + 0.5 EBV dam + MSis as low as 25-35%

• MS = Mendelian sampling in the Meiosis


Meiosis (simplified)

Recombination of chromosome fragments during Meiosis (I)Random allocation of the chromosomes, that were inherited from sire and dam, to the gametes

Large number of different zygotes (offspring) from the same pair of parents Superior sires could only be identified by generating and testing ~ 100 daughters


New opportunities: SNP - genotyping

SNP = Single-nucleotide polymorphismGenotype = Which alleles of the nucleotides A-T,C-G an animal carriesGenome = contains 3 billion base pairsCa. 50.000 SNPs at a cost of about 200 EUR

Anim. n:...AGGCACC GCAATCCACG GAGGCAACGC CCTCACCGGA GGTTTCGCTC TCCACGG... ...AGGCACC GCAATCCACG GAGGCAACGC CCTCACCGGA GGTTTCGCTC TCCACGG...

Anim. 3:...AGGCACC GCAATCCACG GAGGCTACGC CCTCACCGGA GGTTTCGCTC TCCACGG... ...AGGCACC GCAATCCACG GAGGCAACGC CCTCACCGGA GGTTTCGCTC TCCACGG...

Anim. 2: ...AGGCACC GCAATCCACG GAGGCAACGC CCTCACCGGA GGTTTCGCTC TCCACGG......AGGCACC GCAATCCACG GAGGCAACGC CCTCACCGGA GGTTTCGCTC TCCACGG...

Anim. 1: ...AGGCACC GCAATCCACG GAGGCTACGC CCTCACCGGA GGTTTCGCTC TCCACGG......AGGCACC GCAATCCACG GAGGCTACGC CCTCACCGGA GGTTTCGCTC TCCACGG...

Genotype:

TT

AA

AT

AA

Eg position on chromosome 6 # 43.675.239


Use in practical application

Lab is an important part, BUT SNP data has per se no information on ‚traits‘

Steps (eg Meuwissen et al 2001):

Genotype animals that have reliable EBVs from ‚conventional‘ genetic evaluation Calculate regression formulas from a ‚reference population‘ so that SNPs explain well the conventional EBVUse the regression formulas derived by historic data to evaluate young animalsSelect among these young animals


Need for collaboration US / CAN Eurogenomics (Hol: DEU, FRA, NLD, DFS)Intergenomics (BS: AUT, CHE, DEU, FRA, ITA, SLO, USA )

Key factor in application: Size of reference population

Goddard


Genomic EBVs

Statistical models work well

Difficult to have same ranking for cows and bulls with only phenotypic data Genomic plus phenotypic dataImportance of Interbull validation procedure

Implemented in various countriesNeed for converting GEBVs across countriesInterbull activities

• GMACE

Intense use of GEBVs can significantly increase genetic gainGenetic gain doubled (eg Schaeffer 2006)


Increase in reliability of the BVs for AI bulls

Al Bull Milk Yield Al Bull longevity

relia

bilit

y(r

²)

relia

bilit

y(r

²)

Age (years)

GEBVDGVEBV First corp.daug.Second corp. daugt.

GEBVDGVEBV First corp.daug.Second corp. daugt.

Age (years)


Increase in reliability of the BVs: cows

Cow Milk Yield Cow Longevity

Age (years) Age (years)

relia

bilit

y(r

²)


Practical implementation in breeding programs

GEBVs allow to select on the Mendelian sampling term of young animals (without own phenotypes or progeny with phenotypes)GEBV = 0,5 EBV sire + 0.5 EBV dam + MS

This is very good approximated by Σ SNP effects

Strategy old:Identify superior parents

mate them progeny test (with 100 daughters) the highest young candidates

Select among bulls with highest EBVs from progeny testWide use of these bulls

Success of a breeding program was closely linked to population size (test population) of cows under milk recording


Practical implementation in breeding programs

Strategy new:Identify superior animals (r² ~ 75%)

mate them progeny test (100 daughters) the highest young candidates

Select among bulls with highest EBVs from progeny testWide use of these bulls ??

Success of a breeding program is closely linked to Genomic evaluation system ability to calculate GEBVs with high r²Sourcing of promising candidates to calculate GEBVs on themOptimised breeding program to have the

• Optimal number of candidates for genotyping • Optimal selection intensity on them• Number of insemintations per young bull • Etc. (see presentation of Meuwissen in joint ICAR / IB session on genomics)

Success of breeding program is not directly linked to population under milk control any more• Entry for new players in the field


Challenges for breeding programs and herd recording

Rapid developments cause large challenges for cooperative breeding programsTechnological issuesInteraction with the breeders

• As providers of superior genetics eg for AI centres• As users of semen

Availability of phenotypic dataStandard traits:

Necessary for updating the genomic prediction formulaNot directly needed for individual animals breeding value (different to old situation)

New traitsFunctional traits (eg as defined by the ICAR WG ‚Functional Traits‘)New traits e.g. composition of milkhave to be collected on a large number of animals (mostly cows) to first calculate EBVs for these traits and derive prediction formulas for GEBVs

Commercial companies may try to take over part of the cattle breeding industryIssues

• Technology• Patents• Data availability


Areas for future research

Optimal statistical modelStructure and size of reference population

• Incl. Pooling of reference samples across countriesBias due to preselection of bulls on GEBVs in conventional genetic evaluation

Different SNP chips3 K chip available

• Impute from 3 K to 50K use 50 K prediction formula

• Large potential for cows and screening of bullsHD Chip

• Potential for higher reliability (see presentation of A. Eggen)

Best use of GEBVs in breeding programsIncorporation of international genetics in domestic selection decisions(see presentation of J. Dürr about Interbull plans)


Summary

Genomics leads to significant changes in genetic evaluation

Significant changes also in structure of breeding programs

Clear need for more collaborationBest done in the Interbull/ICAR framework

Breeding organisation and herd recording organisations will have to adapt their services and structure to the new opportunities


Thank you for your attention

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